DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding

TL;DR

DSAINet is a dual-scale attentive network designed for general EEG decoding, effectively modeling diverse temporal dynamics across tasks with a lightweight architecture.

Contribution

It introduces a shared spatiotemporal representation and dual-scale attention mechanisms, enabling robust, task-general EEG decoding without task-specific model adjustments.

Findings

Outperforms 13 baseline methods across five EEG decoding tasks.

Achieves high accuracy with only about 77K parameters.

Provides interpretable neurophysiological insights.

Abstract

In real-world applications of noninvasive electroencephalography (EEG), specialized decoders often show limited generalizability across diverse tasks under subject-independent settings. One central challenge is that task-relevant EEG signals often follow different temporal organization patterns across tasks, while many existing methods rely on task-tailored architectural designs that introduce task-specific temporal inductive biases. This mismatch makes it difficult to adapt temporal modeling across tasks without changing the model configuration. To address these challenges, we propose DSAINet, an efficient dual-scale attentive interaction network for general EEG decoding. Specifically, DSAINet constructs shared spatiotemporal token representations from raw EEG signals and models diverse temporal dynamics through parallel convolutional branches at fine and coarse scales. The resulting representations are then adaptively refined by intra-branch attention to emphasize salient scale-specific patterns and by inter-branch attention to integrate task-relevant features across scales, followed by adaptive token aggregation to yield a compact representation for prediction. Extensive experiments on five downstream EEG decoding tasks across ten public datasets show that DSAINet consistently outperforms 13 representative baselines under strict subject-independent evaluation. Notably, this performance is achieved using the same architecture hyperparameters across datasets. Moreover, DSAINet achieves a favorable accuracy-efficiency trade-off with only about 77K trainable parameters and provides interpretable neurophysiological insights. The code is publicly available at https://github.com/zy0929/DSAINet.